deploy: cbe42e3

_sources/index.rst.txt

@@ -14,6 +14,14 @@ Summary
 
 ----
 
+Statement of Need
+-----------------
+`cratepy <https://pypi.org/project/cratepy/>`_ is essentially a numerical tool for any application that requires material multi-scale simulations. Given the intrinsic clustering-based reduced-order modeling approach (e.g., `SCA <https://www.sciencedirect.com/science/article/pii/S0045782516301499>`_, `ASCA <https://www.sciencedirect.com/science/article/pii/S0045782522000895?via%3Dihub>`_), CRATE is mostly useful in applications where the computational cost of standard simulation methods is prohibitive, namely to solve lower-scales in coupled hierarchical multi-scale simulations (e.g., `B.P. Ferreira (2022) <http://dx.doi.org/10.13140/RG.2.2.33940.17289>`_) and to generate large material response databases for data-driven frameworks based on machine learning (e.g., `Bessa et al. (2017) <https://www.sciencedirect.com/science/article/pii/S0045782516314803>`_). Clustering-based reduced-order models achieve a **striking balance between accuracy and computational cost** by first performing a clustering-based domain decomposition of the material model and then solving the equilibrium problem formulated over the resulting reduced model.
+
+In the particular case of a **research environment**, `cratepy <https://pypi.org/project/cratepy/>`_ is designed to easily accommodate further developments, either by improving the already implemented methods or by including new numerical models and techniques. It also provides all the fundamental means to perform comparisons with alternative methods, both in terms of accuracy and computational cost. In a **teaching environment**, `cratepy <https://pypi.org/project/cratepy/>`_ is a readily available tool for demonstrative purposes and/or academic work proposals in solid mechanics and material-related courses.
+
+----
+
 Authorship & Citation
 ---------------------
 CRATE was originally developed by Bernardo P. Ferreira [#]_ in the context of his PhD Thesis [#]_ .

index.html

@@ -114,9 +114,15 @@ <h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headin
 <p><strong>CRATE</strong> (Clustering-based Nonlinear Analysis of Materials) is a Python project (package <a class="reference external" href="https://pypi.org/project/cratepy/">cratepy</a>) developed in the context of computational mechanics to aid the design and development of new materials. Its main purpose is <strong>performing multi-scale nonlinear analyses of heterogeneous materials</strong> through a suitable coupling between first-order computational homogenization and clustering-based reduced-order modeling: given a representative volume element of the material microstructure and the corresponding material phase properties, CRATE computes the material’s effective mechanical response when subject to a prescribed macro-scale loading path.</p>
 </section>
 <hr class="docutils" />
+<section id="statement-of-need">
+<h2>Statement of Need<a class="headerlink" href="#statement-of-need" title="Permalink to this heading">¶</a></h2>
+<p><a class="reference external" href="https://pypi.org/project/cratepy/">cratepy</a> is essentially a numerical tool for any application that requires material multi-scale simulations. Given the intrinsic clustering-based reduced-order modeling approach (e.g., <a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S0045782516301499">SCA</a>, <a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S0045782522000895?via%3Dihub">ASCA</a>), CRATE is mostly useful in applications where the computational cost of standard simulation methods is prohibitive, namely to solve lower-scales in coupled hierarchical multi-scale simulations (e.g., <a class="reference external" href="http://dx.doi.org/10.13140/RG.2.2.33940.17289">B.P. Ferreira (2022)</a>) and to generate large material response databases for data-driven frameworks based on machine learning (e.g., <a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S0045782516314803">Bessa et al. (2017)</a>). Clustering-based reduced-order models achieve a <strong>striking balance between accuracy and computational cost</strong> by first performing a clustering-based domain decomposition of the material model and then solving the equilibrium problem formulated over the resulting reduced model.</p>
+<p>In the particular case of a <strong>research environment</strong>, <a class="reference external" href="https://pypi.org/project/cratepy/">cratepy</a> is designed to easily accommodate further developments, either by improving the already implemented methods or by including new numerical models and techniques. It also provides all the fundamental means to perform comparisons with alternative methods, both in terms of accuracy and computational cost. In a <strong>teaching environment</strong>, <a class="reference external" href="https://pypi.org/project/cratepy/">cratepy</a> is a readily available tool for demonstrative purposes and/or academic work proposals in solid mechanics and material-related courses.</p>
+</section>
+<hr class="docutils" />
 <section id="authorship-citation">
 <h2>Authorship &amp; Citation<a class="headerlink" href="#authorship-citation" title="Permalink to this heading">¶</a></h2>
-<p>CRATE was originally developed by Bernardo P. Ferreira <a class="footnote-reference brackets" href="#id3" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> in the context of his PhD Thesis <a class="footnote-reference brackets" href="#id4" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a> .</p>
+<p>CRATE was originally developed by Bernardo P. Ferreira <a class="footnote-reference brackets" href="#id6" id="id4" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> in the context of his PhD Thesis <a class="footnote-reference brackets" href="#id7" id="id5" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a> .</p>
 <p>If you use CRATE in a scientific publication, it is appreciated that you cite this PhD Thesis:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@phdthesis</span><span class="p">{</span><span class="n">ferreira</span><span class="p">:</span><span class="mi">2022</span><span class="n">a</span><span class="p">,</span>
   <span class="n">title</span> <span class="o">=</span> <span class="p">{</span><span class="n">Towards</span> <span class="n">Data</span><span class="o">-</span><span class="n">driven</span> <span class="n">Multi</span><span class="o">-</span><span class="n">scale</span> <span class="n">Optimization</span> <span class="n">of</span> <span class="n">Thermoplastic</span> <span class="n">Blends</span><span class="p">:</span> <span class="n">Microstructural</span> <span class="n">Generation</span><span class="p">,</span> <span class="n">Constitutive</span> <span class="n">Development</span> <span class="ow">and</span> <span class="n">Clustering</span><span class="o">-</span><span class="n">based</span> <span class="n">Reduced</span><span class="o">-</span><span class="n">Order</span> <span class="n">Modeling</span><span class="p">},</span>
@@ -129,12 +135,12 @@ <h2>Authorship &amp; Citation<a class="headerlink" href="#authorship-citation" t
 </pre></div>
 </div>
 <aside class="footnote-list brackets">
-<aside class="footnote brackets" id="id3" role="note">
-<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<aside class="footnote brackets" id="id6" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id4">1</a><span class="fn-bracket">]</span></span>
 <p><a class="reference external" href="https://www.linkedin.com/in/bpferreira/">LinkedIn</a> , <a class="reference external" href="https://orcid.org/0000-0001-5956-3877">ORCID</a>, <a class="reference external" href="https://www.researchgate.net/profile/Bernardo-Ferreira-11?ev=hdr_xprf">ResearchGate</a></p>
 </aside>
-<aside class="footnote brackets" id="id4" role="note">
-<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<aside class="footnote brackets" id="id7" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id5">2</a><span class="fn-bracket">]</span></span>
 <p>Ferreira, B.P. (2022). <em>Towards Data-driven Multi-scale
 Optimization of Thermoplastic Blends: Microstructural
 Generation, Constitutive Development and Clustering-based